🎬 Movie Review Sentiment Analysis

🔗 Live Demo: Movie Review Sentiment Analysis App

📌 Description

This project focuses on analyzing sentiment in movie reviews using machine learning techniques. The dataset used is the IMDB Reviews Dataset, which contains labeled reviews as either positive or negative. The goal is to preprocess text, train machine learning models, and develop an interactive web app for sentiment prediction.

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🚀 Features

• 📊 Data Processing: Text preprocessing including tokenization, stopword removal, and vectorization.
• 🤖 Machine Learning Models: Logistic Regression, Multinomial NB and Random Forest with TF-IDF vectorization.
• 📈 Model Evaluation: Accuracy and F1-score used to assess performance.
• 🌐 Interactive Web App: Built with Streamlit for user-friendly sentiment analysis.
• 🕙 Real-Time Predictions: Enter a movie review and get instant results.

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📂 Dataset

• Kaggle Dataset: IMDB Dataset of 50K Movie Reviews
• Includes: Movie reviews labeled as positive or negative

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🛠 Models Used

• ✅ Logistic Regression
• ✅ Random Forest
• ✅ Multinomial NB

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📊 Results & Evaluation

• Accuracy & F1-Score used for performance comparison.
• The model predicts whether a given movie review is positive or negative.

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📖 Jupyter Notebook

• File: movie-review-sentiment-analysis.ipynb
• Includes:
  • Data exploration & preprocessing
  • Feature extraction using TF-IDF
  • Model training & evaluation
  • Saving the best accuracy trained model for deployment

Run it with:

jupyter notebook movie-review-sentiment-analysis.ipynb

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⚡ Usage

1️⃣ Clone the repository

git clone https://github.com/Zeeshier/Movie-Review-Sentiment-Analysis
cd movie-review-sentiment-analysis

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Launch the Streamlit app

streamlit run app.py

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